15 - Data - BI

ETL - DWH - Dashboard - ML

Data is plentiful. Decisions are missing.

-- Definition

BI -- Business Intelligence. The layer that brings together, cleans, summarizes and turns a company's scattered data into a decision support system. In Senkronix data projects we take raw ERP, CRM, e-commerce and web analytics data, run it through ETL pipelines, unify it in a data warehouse, and build interactive dashboards and ML predictions on top.

N° I -- Why Custom?

Reports exist.
But insight doesn't.

Most organizations are rich in data: the ERP knows sales, CRM knows customers, e-commerce knows behavior, web analytics knows traffic. But none of them know each other. The Excel report copy-pasted together once a week is already out of date. Executives cannot get an instant answer to "what is the full value of this customer's profile?"

In Senkronix BI projects the goal is to establish a single source of truth. Data flows automatically from every system, unifies in a single data warehouse, is processed through business logic, and surfaces in dashboards. Executives see real-time metrics over morning coffee. Business units find answers to their own questions from their own dashboards. Machine learning models generate forward-looking predictions.

Advantages of a custom solution

Single source of truth -- ERP, CRM, e-commerce and web united in one data warehouse
Real-time ETL -- no waiting, reports always current
Self-service BI -- each business unit builds its own dashboard
Predictive analytics -- forecasts for sales, inventory, customer churn
GDPR/KVKK-compliant data governance -- role-based access, anonymization
Power BI, Tableau, Grafana or custom -- the right tool for the need

N° II -- Modules

Six layers.
From raw data to decisions.

-- ETL Pipeline / N° II-A

Five stages · From source to decision

● ETL stage→ Data flow

Data Integration

ERP, CRM, e-commerce, POS, web analytics -- automated extraction from every source (ETL/ELT).

Data Warehouse (DWH)

Snowflake, BigQuery, PostgreSQL, ClickHouse -- up to petabyte scale, star/snowflake schema.

Data Cleansing

Missing data, outliers, duplicate detection; normalization and enrichment based on business rules.

Dashboards

Power BI, Tableau, Metabase, Superset or custom dashboards; role-based views.

Predictive Analytics

Time series (Prophet, ARIMA), segmentation (k-means, RFM), churn prediction, recommendation engines.

AI / ML Models

Predictions via tailored models; Python (scikit-learn, TensorFlow, PyTorch), MLOps.

N° III -- Integrations & Technology

Every data source,
every tool.

-- Source System Map / N° III-A

Six sources · BI core

● BI core□ Source system

The value of a data project is directly proportional to the variety of sources. Senkronix BI solutions connect to whatever exists in your organization: from ERP to machines to sensors, from Facebook Ads to call-center logs, everything unites in the same data warehouse.

Data sources

ERP: Logo, Mikro, Netsis, SAP; via API, database or file
CRM: Senkronix CRM, Salesforce, HubSpot; activity and sales funnel data
E-commerce: Trendyol, Hepsiburada, Shopify, WooCommerce; orders and catalog
POS: Register sales, branch summary, hourly traffic
Advertising: Google Ads, Meta Ads, TikTok Ads, LinkedIn Ads; campaign cost and conversion
Web: Google Analytics 4, Adobe Analytics, GTM; traffic and behavior
IoT / SCADA: Sensor data, production machinery, energy meters

Data warehouse and processing

Cloud DWH: Snowflake, BigQuery, Redshift, Azure Synapse
On-prem DWH: PostgreSQL, ClickHouse, Oracle
ETL/ELT: Airbyte, Fivetran, Airflow, dbt; code-based data transformations
Stream: Kafka, Redpanda; real-time data flow

Visualization and ML

BI tools: Power BI, Tableau, Looker, Metabase, Superset, Grafana
ML: scikit-learn, TensorFlow, PyTorch, XGBoost; Python + Jupyter
MLOps: MLflow, DVC, Airflow -- model deployment and versioning
Custom dashboard: React + D3.js + Plotly; on-brand and high performance

N° IV -- Who is it for?

Any department with data.

Scenario - 01

Board / Executive

Enterprise KPI dashboard, revenue vs. cost, location comparison, target-versus-actual tracking.

Scenario - 02

Sales & Marketing

Sales funnel analysis, customer segmentation, campaign ROI, attribution, LTV calculations.

Scenario - 03

Finance & Accounting

Cash flow, collection delay, cost centers, budget versus actuals.

Scenario - 04

Operations & Production

OEE, production efficiency, scrap rate, inventory turnover, supplier performance.

Scenario - 05

Human Resources

Employee turnover, performance, training, hiring funnel, payroll analytics.

Scenario - 06

R&D & Product

A/B test results, feature adoption, user behavior analysis, roadmap prioritization.

N° V -- Frequently Asked

Clear questions,
clear answers.

We already use Power BI -- what does Senkronix add?+

Power BI is an excellent visualization tool, but not sufficient on its own. The ETL layer behind it (extract, clean, combine) is a separate discipline. We build the full stack: ETL + data warehouse + Power BI data model + custom dashboard business logic + data security. Power BI then produces fast, accurate reports.

How much data does it support?+

For small setups (GB scale) PostgreSQL is enough. Mid-scale (TB) calls for ClickHouse or a cloud DWH. Large scale (PB) uses distributed architectures such as Snowflake or BigQuery. Years of historical data together with new streams can be managed in a single structure.

How is data privacy (KVKK/GDPR) handled?+

Personal data enters the warehouse with a specific classification. Role-based access limits who can see what. Anonymization and pseudonymization allow analytics without exposing personal identity. Retention periods are enforced automatically, and deletion-request workflows are ready out of the box.

How reliable are machine learning models?+

No ML model is 100% accurate. We always present predictions with a confidence interval. For example, a churn prediction: "this customer's churn probability is 72% ± 8%". The business decision still belongs to humans -- the model provides information and speed. Model performance is monitored continuously and retraining happens when drift appears.

How does self-service BI work?+

We design for business units to ask their own questions. The sales team can filter "what was this month's rep performance?" without writing any code. With the data model and terminology defined in advance, it is difficult to build a wrong query.

Is real-time data possible?+

Yes. Stream processing (Kafka + ClickHouse) enables sub-second-latency dashboards. It makes sense for production lines or trading. For classic BI, 5-15 minute "near real-time" is usually enough.

Cost and timeline?+

A single-report project takes 1-2 months, a mid-scale enterprise BI 4-6 months, a large data warehouse project 8-14 months. Cost is finalized during Discovery based on source count, warehouse size, number of dashboards and ML model complexity. Usage-based fees apply when a cloud DWH is used.

N° VI -- Process

Four stages.
Each one documented.

Discovery

Requirements analysis, on-site observation and scope definition. A documented scope statement is delivered.

Output: SRS - BPMN - Scope

Design

Architecture, data model, API, interface prototypes. Every decision is approved before code is written.

Output: UML - Figma - API Spec

Development

Two-week sprints with demos each sprint, CI/CD, code review and automated testing.

Output: Demo - Git - CI/CD

Launch & Support

Go-live, training, documentation. Long-term support is fundamental, governed by an SLA.

Output: Go-Live - SLA

-- Data & BI Proposal Desk

Describe your data.
To the decision table -- let's bring it there together.

Request BI Proposal 444 27 14

bilgi@senkronix.com - Karatay / Konya